Smoking and salivary microbiota: a cross-sectional analysis of an Italian alpine population

The oral microbiota plays an important role in the exogenous nitrate reduction pathway and is associated with heart and periodontal disease and cigarette smoking. We describe smoking-related changes in oral microbiota composition and resulting potential metabolic pathway changes that may explain smoking-related changes in disease risk. We analyzed health information and salivary microbiota composition among 1601 Cooperative Health Research in South Tyrol participants collected 2017–2018. Salivary microbiota taxa were assigned from amplicon sequences of the 16S-V4 rRNA and used to describe microbiota composition and predict metabolic pathways. Aerobic taxa relative abundance decreased with daily smoking intensity and increased with years since cessation, as did inferred nitrate reduction. Former smokers tended to be more similar to Never smokers than to Current smokers, especially those who had quit for longer than 5 years. Cigarette smoking has a consistent, generalizable association on oral microbiota composition and predicted metabolic pathways, some of which associate in a dose-dependent fashion. Smokers who quit for longer than 5 years tend to have salivary microbiota profiles comparable to never smokers.

Filtered Taxa and samples.This was the phyloseq used for the actual anlyses For further details, see Suppl.Tables 2 and 4 phyloseq-class experiment-level object otu_table() OTU     5. Some genera are exclusively significant in sex-separated models, but are all found in the sex-adjusted significant genera.Venn diagram of the significant genera found in each of the three models.In all cases, the contrast chosen was Current/Never, adjusting for age and number of teeth.All significance thresholds were q-values < 0.05, all Benjamini-Hochberg (5% FDR) except for ALDEx2, which uses Holm correction.

Supplementary Figure 7 -Correlation of differentially abundant pathways
-independence of number of teeth from age groups and smoking (A) Relationship between age and number of teeth.Numbers are reported as proportions for each age category.(B) Relationship between lifetime exposure to smoke (lifetime pack-years) and the number of teeth in only current smokers.Individuals were further split into age groups to control for age-related tooth loss.Lifetime pack years, a proxy of a lifetime exposure to the tobacco smoked, were calculated in Former and Current smokers as follows:Lif etime P ack-Y ear equivalents =cigarettes day 20 × 365 days × years smoked of relative abundance composition of CHRISMB samples included in this study (N = 1601) Each bar is a sample.Samples were sorted from left to right based on the decreasing relative abundance of the most abundant taxa overall (A) Phylum level, samples were sorted by decreasing abundance of Firmicutes; (B) Genus level, samples were sorted by decreasing abundance of Prevotella.diversity and richness estimates in relation to smoking (colours) subdivided by the number of teeth (x axis) (A) Shannon diversity; (B) Inverse Simpson diversity; (C) Observed number of taxa; (D) Chao 1 Richness.Estimates were calculated on samples rarefied to 5000 counts per sample.
l y − g l y c e r o l ) b i o s y n t h e s i s 2−methylcitrate cycle I 2−methylcitrate cycle II aromatic biogenic amine degradation (bacteria) fatty acid &beta;−oxidation I fatty acid salvage glutaryl−Positive bacteria are more abundant in the saliva of smokers, regardless of reduction.Phyla were annotated based on a manually curated

Table 1 :
Shannon Diversity/Richness metric in relation to other variables considered in the study.Significance was estimated with the 'stats::lm' linear regression function modeling each alpha metric against the following variables in the model: age, sex, Number of Teeth, Smoking Status.No, transformation was applied to any variable considered

Table 2 :
InvSimpson Diversity/Richness metric in relation to other variables considered in the study.Significance was estimated with the 'stats::lm' linear regression function modeling each alpha metric against the following variables in the model: age, sex, Number of Teeth, Smoking Status.No, transformation was applied to any variable considered

Table 3 :
Observed Diversity/Richness metric in relation to other variables considered in the study.Significance was estimated with the 'stats::lm' linear regression function modeling each alpha metric against the following variables in the model: age, sex, Number of Teeth, Smoking Status.

Table 4 :
Chao1 Diversity/Richness metric in relation to other variables considered in the study.Significance was estimated with the 'stats::lm' linear regression function modeling each alpha metric against the following variables in the model: age, sex, Number of Teeth, Smoking Status.No, transformation was applied to any

-Sex specific differentially abundant genera with Smok- ing
table made by G.A., since Gram staining generally differentiates at phylum level.The results were presented like the oxygen metabolism on Figure 1 (Main Text) and Supplementary Figure 6-B.